Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

Pre-requisites

To enrol students are required to have taken COMP11120 (waived for CM students) plus COMP27112.

Assessment methods

90% Written exam

10% Coursework

Timetable

Semester

Event

Location

Day

Time

Group

Sem 2

Lecture

1.3

Mon

11:00 - 12:00

-

Sem 2

Lecture

1.4

Fri

12:00 - 13:00

-

Sem 2

Lab

1.8

Mon

14:00 - 15:00

-

Themes to which this unit belongs

Visual Computing

Aims

To provide a broad introduction to Computer Vision and Image Interpretation. To introduce the basic concepts and algorithmic tools of Computer Vision To explore the importance of modelling and representation in interpretation of images. To provide an understanding of the range of processing components involved in image interpretation systems.

Syllabus

The tools and algorithms of computer vision are introduced in the context of two major capabilities required of visual systems: recognition - finding and identifying expected things in images and 3D interpretation - understanding a dynamic 3D scene from 2D images or sequences of images. These capabilities are explored using applications of varying levels of complexity: recognising man-made objects, interpreting medical images, face recognition, robotics, scene reconstruction and surveillance.

Students taking the module will require some basic familiarity with matrix and vector algebra, such as that covered in MT1662 or MT1672. Tutorial material will be provided where the mathematics goes beyond the scope of those modules. A general familiarity with basic concepts of calculus (integration, partial differentiation) will also be useful.

Introduction:

The role of Computer Vision, applications, successes, research issues; its relationship to natural vision, basic image properties.

In addition to the material in lecture notes and textbooks, Self-test questions and solutions will be provided. For some topics, practical exercises, with associated MATALB scripts and images will be available for use by students unsupervised. These additional materials may be downloaded from the course web site.

Teaching methods

Lectures

22

Feedback methods

Written feedback is provided on 6 pieces of coursework throughout the course, corresponding to the major topics covered.